Operations Research for Unmanned Systems ebook

Table of Contents

Cover

Title Page

About the Contributors

Acknowledgements

1 Introduction

1.1 Introduction

1.2 Background and Scope

1.3 About the Chapters

References

2 The In-Transit Vigilant Covering Tour Problem for Routing Unmanned Ground Vehicles

2.1 Introduction

2.2 Background

2.3 CTP for UGV Coverage

2.4 The In-Transit Vigilant Covering Tour Problem

2.5 Mathematical Formulation

2.6 Extensions to Multiple Vehicles

2.7 Empirical Study

2.8 Analysis of Results

2.9 Other Extensions

2.10 Conclusions

Author Statement

References

3 Near-Optimal Assignment of UAVs to Targets Using a Market-Based Approach

3.1 Introduction

3.2 Problem Formulation

3.3 Literature

3.4 The Market-Based Solution

3.5 Results

3.6 Recommendations for Implementation

3.7 Conclusions

Appendix 3.A A Mixed Integer Linear Programming (MILP) Formulation

References

4 Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles

4.1 Background

4.2 Assumptions

4.3 Measures of Performance

4.4 Preliminary Results

4.5 Concepts of Operations

4.6 Optimality with Two Different Angular Observations

4.7 Optimality with N Different Angular Observations

4.8 Modeling and Algorithms

4.9 Random Search Formula Adapted to AUVs

4.10 Mine Countermeasures Exploratory Operations

4.11 Numerical Results

4.12 Non-uniform Mine Density Distributions

4.13 Conclusion

Appendix 4.A Optimal Observation Angle between Two AUV Legs

Appendix 4.B Probabilities of Detection

References

5 Optical Search by Unmanned Aerial Vehicles

5.1 Introduction

5.2 Search Planning for Unmanned Sensing Operations

5.3 Results

5.4 Conclusions

Acknowledgments

References

6 A Flight Time Approximation Model for Unmanned Aerial Vehicles

Nomenclature

6.1 Introduction

6.2 Problem Statement

6.3 Literature Review

6.4 Flight Time Approximation Model Development

6.5 Additional Task Types

6.6 Adding Wind Effects

6.7 Computational Expense of the Final Model

6.8 Conclusions and Future Work

Acknowledgments

References

7 Impacts of Unmanned Ground Vehicles on Combined Arms Team Performance

7.1 Introduction

7.2 Study Problem

7.3 Study Methods

7.4 Study Results

7.5 Discussion

References

8 Processing, Exploitation and Dissemination

8.1 Introduction

8.2 Background

8.3 Analysis

8.4 Conclusion

Acknowledgments

Appendix 8.A

References

9 Analyzing a Design Continuum for Automated Military Convoy Operations

9.1 Introduction

9.2 Definition Development

9.3 Automation Continuum

9.4 Mathematically Modeling Human Input Proportion (H) versus System Configuration

9.5 Mathematically Modeling Robotic Decision-Making Ability (R) versus System Configuration

9.6 Mathematically Modeling H and R

9.7 Conclusion

9.A System Configurations

10 Experimental Design for Unmanned Aerial Systems Analysis

10.1 Introduction

10.2 Some UAS History

10.3 Statistical Background for Experimental Planning

10.4 Planning the UAS Experiment

10.5 Applications of the UAS Planning Guidelines

10.6 Conclusion

Acknowledgments

Disclaimer

References

11 Total Cost of Ownership (TOC)

11.1 Introduction

11.2 Life Cycle Models

11.3 Cost Estimation Methods

11.4 UMAS Product Breakdown Structure

11.5 Cost Drivers and Parametric Cost Models

11.6 Considerations for Estimating Unmanned Ground Vehicle Costs

11.7 Additional Considerations for UMAS Cost Estimation

11.8 Conclusion

Acknowledgments

References

12 Logistics Support for Unmanned Systems

12.1 Introduction

12.2 Appreciating Logistics Support for Unmanned Systems

12.3 Challenges to Logistics Support for Unmanned Systems

12.4 Grouping the Logistics Challenges for Analysis and Development

12.5 Further Considerations

12.6 Conclusions

References

13 Organizing for Improved Effectiveness in Networked Operations

13.1 Introduction

13.2 Understanding the IACM

13.3 An Agent-Based Simulation Representation of the IACM

13.4 Structure of the Experiment

13.5 Initial Experiment

13.6 Expanding the Experiment

13.7 Conclusion

Disclaimer

References

14 An Exploration of Performance Distributions in Collectives

14.1 Introduction

14.2 Who Shoots How Many?

14.3 Baseball Plays as Individual and Networked Performance

14.4 Analytical Questions

14.5 Imparity Statistics in Major League Baseball Data

14.6 Conclusions

Acknowledgments

References

15 Distributed Combat Power

15.1 Introduction

15.2 Salvo Theory

15.3 Salvo Warfare with Unmanned Systems

15.4 The Salvo Exchange Set and Combat Entropy

15.5 Tactical Considerations

15.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 Results for

n

 = 20 and

p

 = 5.

Table 2.2 Results for

n

 = 20 and

p

 = 10.

Table 2.3 Results for

n

 = 30 and

p

 = 5.

Table 2.4 Results for

n

 = 30 and

p

 = 10.

Table 2.5 Number of infeasible solutions for each combination of data sets.

Table 2.6 Comparison of performance between the CTP and VCTP models.

Table 2.7 Percentage of savings in average tour lengths.

Table 2.8 Percentage of targets covered by edges in the VCTP model.

Table 2.9 Number of iterations by both models and percentage comparison.

Chapter 03

Table 3.1 Comparison of the average optimality gap obtained by the market-based solution (MBS) to Carlsson and ant colony optimization (ACO) for the

Min-Max

case.

Table 3.2 Comparison of the average runtime in seconds required by the market-based solution (MBS) to Carlsson and ant colony optimization (ACO) for the

Min-Max

case.

Table 3.3 Comparison of the market-based solution (MBS) to Carlsson and fuzzy clustering (FCL) for the

Min-Max

case with 4 depots, 16 UAVs, and 1000 targets.

Chapter 04

Table 4.1 Scenario parameters.

Table 4.2 Johnson parameters defining the range and angle probability curves.

Table 4.3 Probability of detection for a search time of 8 h.

Chapter 06

Table 6.1 Average fuel burn rate factor levels.

Table 6.2 Waypoint data used in the flight time approximation model.

Table 6.3 Summary of the cases used in the runtime analysis.

Table 6.4 Waypoint data obtained from the flight time approximation model.

Table 6.5 Runtime analysis results for all three cases.

Table 6.6 Results of the simple mission plan with flight times and fuel burn rates.

Chapter 07

Table 7.1 Experimental options, with the options explored highlighted.

Table 7.2 Summary of EXFOR replication casualties.

Table 7.3 Summary of OPFOR CL simulation casualties.

Table 7.4 Ranking of casualties by CL simulation.

Table 7.5 Summary of EXFOR replication casualties.

Table 7.6 Summary of OPFOR replication casualties.

Table 7.7 Ranking of casualties by replications.

Table 7.8 Option ranking by single measures for low density urban terrain.

Table 7.9 Options ranked by complete measures for low density urban terrain.

Table 7.10 Ranking by single measures for dense urban terrain.

Table 7.11 Options ranked by complete measures for dense urban terrain.

Table 7.12 Performance ranking using complete measures.

Table 7.13 Performance rankings comparing casualty and complete measures.

Chapter 08

Table 8.1 Parameter values, for example scenario, broadly indicative of an unmanned aerial vehicle searching for a mobile target on land when there is a counter-air threat.

Chapter 09

Table 9.1 The first 10 trials of the Monte Carlo simulation for the DA, LF1, and LF2 configurations.

Table 9.2 The first 10 trials of the Monte Carlo simulation for the LF3, LF4, and WA1 configurations.

Table 9.3 The first 10 trials of the Monte Carlo simulation for the WA2, FA1, and FA2 configurations.

Table 9.4 Human input proportion (H) values from the Monte Carlo simulation with respect to configuration.

Table 9.5 Human input proportion (H) versus system configuration, computed from Eq. (9.6).

Table 9.6 Equations (9.8) and (9.9) to computation for values for x, as y varies between 0 and 1.

Table 9.7 The R

i

values for the DW, DA, LF1, and LF2 system configurations.

Table 9.8 The R

i

values for the FA3, FA4, and WA1 system configurations.

Table 9.9 The R

i

values for the WA2, FA1, and FA2 system configurations.

Table 9.10 The results of the robotic decision-making ability (R) vs. system configuration.

Table 9.11 The results of the robotic decision-making ability (R) when weighted by H versus system configuration.

Table 9.12 Using Eq. (9.14), which models R versus system configuration when weighted by H, to compute values of the dependent variable y.

Table 9.13 Using Eq. (9.16) (second column) and Eq. (9.17) (third column) to compute values for x, which represents the various system configurations, as y varies between 0 and 1.

Table 9.14 The results of calculation H vs. system configuration and R (weighted by H) vs. system configuration.

Table 9.15 Using Eq. (9.20), which models H vs. R, to compute values of the dependent variable y.

Chapter 11

Table 11.1 Air system (Unmanned Air System (UAS)).

Table 11.2 Ground system (Unmanned Ground System (UGS)).

Table 11.3 Ground system (UGS).

Table 11.4 Marine system (UGS).

Table 11.5 Material composition rating scale [17].

Table 11.6 Certification level rating scale [17].

Table 11.7 Production tools and practices rating scale [17].

Table 11.8 Team cohesion rating scale.

Table 11.9 Programmer capability rating scale.

Table 11.10 Number of requirements rating scale.

Table 11.11 Technology risk rating scale.

Table 11.12 Process capability rating scale.

Table 11.13 Performance-based characteristics rating scale.

Table 11.14 Performance cost drivers.

Table 11.15 Weight-based cost drivers.

Table 11.16 Logistics cost driver.

Table 11.17 Training cost driver considerations.

Table 11.18 Manned unmanned systems teaming cost driver.

Table 11.19 Types of estimates needed per product breakdown structure element.

Chapter 12

Table 12.1 Comparison of US, Australian, and UK military principles of logistics.

Table 12.2 Mission expansion considerations from US Department of Defense [36].

Table 12.3 Grouping unmanned systems for the purposes of analyzing logistics systems.

Chapter 13

Table 13.1 Types of links available in the IACM.

Table 13.2 All possible configurations of a 4-3-4-1 network.

Table 13.3 The numbers of meaningfully different configurations of all X-Y-X-1 networked forces where X < 11 and Y < 8.

Table 13.4 The numbers of unique λ

PFE

values of all X-Y-X-1 networked forces where X < 11 and Y < 8.

Chapter 14

Table 14.1 Measures of baseball performance.

Table 14.2 Imparity statistics for the 2013 MLB regular season.

Table 14.3 Imparity statistics for MLB infielders, 2013 data.

Table 14.4 Imparity statistics for MLB outfielders, 2013 data.

List of Illustrations

Chapter 02

Figure 2.1 Possible solution for a CTP.

Figure 2.2 Optimal solution for a CTP and a VCTP.

Figure 2.3 In-transit vigilant coverage by edge (

v

i

, v

j

) on target

w

k

.

Figure 2.4 Lack of coverage by edge (

v

i

, v

j

) on target

w

k

.

Figure 2.5 Comparison of CTP (a) and VCTP (b) solution for data set C101.

Figure 2.6 Comparison of CTP (a) and VCTP (b) solution for data set R101.

Chapter 03

Figure 3.1 (a) A map with three depots, denoted by squares and eight targets, denoted by circles. (b) A time-varying target benefit value, where the benefit decreases over time to account for limited target lifetime.

Figure 3.2 Solution development. (a) Initial solution with coarse clustering. (b) Assignment of finer clusters. (c) Assignment of small clusters. (d) Final solution and assignment of individual targets. The circle radii are proportional to the number of cities in the cluster.

Figure 3.3 An example of a maximum profit tour that is not a minimum cost tour. Graph axes represent units of distance.

Figure 3.4 Comparison of the MBS to mathematical optimization of the

Min-Sum

case. (a) Optimality gap. (b) Relative runtime.

Figure 3.5 Comparison of the MBS to mathematical optimization of the

Min-Max

case. (a) Optimality gap. (b) Relative runtime.

Figure 3.6 A

Min-Max

solution for 10 UAVs and 5000 targets. Runtime was about 12 000 s on a computer with i7, 3.4 GHz CPU with 16 GB RAM.

Figure 3.7 Comparison of the MBS to mathematical optimization in the

Max-Pro

case for time-invariant and equal benefit values. (a) Optimality gap. (b) Relative runtime.

Figure 3.8 Comparison of the MBS to mathematical optimization in the

Max-Pro

with time-invariant and three benefit values case. (a) Optimality gap. (b) Relative runtime.

Figure 3.9 A scenario depicting

Max-Pro

with time-varying target benefits. (a) The solution found by MBS. (b) The values of the target benefits vs. time.

Figure 3.10 General implementation scheme.

Chapter 04

Figure 4.1 Search areas (Gulf of La Spezia, Italy).

Figure 4.2 The

Dorado

.

Figure 4.3 Coordinate system.

Figure 4.4 Probability of detection as a function of range (LHS) and angle (RHS).

Figure 4.5 Mine observed at 85° (LHS) and at 0° (RHS).

Figure 4.6 Uneven lawn-mowing pattern.

Figure 4.7 Probability of detecting at least one mine vs. number of searched cells.

Figure 4.8 (a) Search with one AUV – even lawn-mowing pattern (LHS), uneven lawn-mowing pattern (middle), and zigzag pattern (RHS). (b) Search with two AUVs – even lawn-mowing patterns (LHS), uneven lawn-mowing patterns (middle), and zigzag patterns (RHS).

Figure 4.9 Optimal path (dotted) in a sub-box area.

Chapter 05

Figure 5.1 Ratio between the flight scale and pixel size (GSD) for each image. Open circles are the UAV positions from take-off until reaching the planned height.

Figure 5.2 Ratio between flight height and pixel size (GSD) for each image. Open circles are the UAV positions from take-off until reaching the planned height.

Figure 5.3 “E-300 viewer” platform moments before take-off.

Figure 5.4 Ratio between projection center heights above sea level and projection center heights above the ground. Points below 600 m (open circles) represent the UAV positions from take-off until reaching the planned height for image capturing.

Figure 5.5 Results of the pixel size (cm) analysis control. Location of the pilot area marked in blue.

Figure 5.6 Results of the overlap between flight paths analysis.

Figure 5.7 Pilot area mosaic (153 images from five flight paths, on PNOA orthophotograph).

Figure 5.8 Positional differences and geometric limitations between one of the geo-referenced images (red elements) versus PNOA orthophotograph (green).

Figure 5.9 Imaging results from the pilot mosaic. Elements (animals) detected through digital processing and classification.

Figure 5.10 Results on the pilot area. Potential individuals detected on the thermal images mosaic and the PNOA orthophotograph.

Figure 5.11 Results on the pilot mosaic of locating probable (in green) and non-probable (in red) individuals in the study area (refined vectorization).

Figure 5.12 Location of areas with probable presence of animals after the classification, processing, and interpretation of the images.

Chapter 06

Figure 6.1 (a) Path from Dubins aircraft model and (b) path from Dubins curve path model.

Figure 6.2 An

i

,

j

,

k

example of the radius of sight task.

Figure 6.3 Segmentation of the radius of sight task.

Figure 6.4 The radius of sight task heading sets. (a) shows the headings that are parallel to the heading from

i

to

j

and (b) shows the headings that are parallel the heading from

i

to

k

for each of the four entry points.

Figure 6.5 The circular loitering path task.

Figure 6.6 Choosing four equally likely points out of eight possible points.

Figure 6.7 Determining the exit points based on entry point location and duration.

Figure 6.8 Breaking the flight path from

j

to

k

into linear segments.

Figure 6.9 Determining the components of wind acting on the UAV.

Figure 6.10 An eight-waypoint mission with a resource base.

Chapter 07

Figure 7.1 Low density urban environment.

Figure 7.2 High density urban environment.

Figure 7.3 Experimental force.

Figure 7.4 Opposition force.

Figure 7.5 Activity process.

Figure 7.6 Plot of ordinal rankings of key manned vehicle characteristics.

Chapter 08

Figure 8.1 Operations to acquire a difficult target in an anti-access/area-denial scenario. When searching, the unmanned vehicle must communicate and is thus susceptible to counter-acquisition. The vehicle may intermittently have to hide and then resume its search.

Figure 8.2 Performance required for automation to be rational in the example scenario. Even if automation performs less well than a human, it will regain effectiveness from increased survivability.

Figure 8.3 Reducing the time to resume search raises the performance threshold for automation to be rational (solid line sets

, dotted line sets

). The advantage of automation is in reducing the need to hide. As the cost of hiding reduces, this advantage is eroded.

Figure 8.4 Increasing the time to between the target ceding glimpses raises the performance threshold for automation to be rational (solid line sets

, dotted line sets

). If the target cedes fewer glimpses, then automation has to do better with those glimpses to compensate.

Chapter 09

Figure 9.1 Mathematical model of human input frequency (H) vs. system configuration. Series 1 represents the H value for each system configuration, and the solid and dashed trend lines represent sixth ordered and second ordered polynomial regression equations, respectively.

Figure 9.2 Mathematical model of human input frequency (H) vs. system configuration for Regime 1. Series 1 represents the cumulative H value for each system configuration.

Figure 9.3 Mathematical model of human input frequency (H) vs. system configuration for Regime 2. Series 1 represents the cumulative H value for each system configuration and the solid trend line represents a third ordered polynomial regression equation.

Figure 9.4 Mathematical model of human input frequency (H) vs. system configuration for Regime 3. Series 1 represents the cumulative H value for each system configuration and the solid trend line represents a third ordered polynomial regression equation.

Figure 9.5 Mathematical model of robotic decision-making ability (R) vs. system configuration. Series 1 represents the cumulative R value for each system configuration, and the solid and dashed trend lines represent sixth ordered and second ordered polynomial regression equations, respectively.

Figure 9.6 Mathematical model of robotic decision-making ability (R) vs. system configuration weighted by H. The bar chart represents the cumulative R value for each system configuration when weighted by H, and the solid and dashed trend lines represent sixth ordered and second ordered polynomial regression equations, respectively.

Figure 9.7 Mathematical model of R (weighted by H) vs. system configuration for Regime 1. The bar chart represents the cumulative R value for each system configuration.

Figure 9.8 Mathematical model of R (weighted by H) vs. system configuration for Regime 2. Series 1 represents the cumulative R value for each configuration and the trend line is a third ordered polynomial regression equation.

Figure 9.9 Mathematical model of R (weighted by H) vs. system configuration for Regime 3. Series 1 represents the cumulative R value for each system configuration and the solid trend line represents a third ordered polynomial regression equation.

Figure 9.10 Summary of the results from Figure 9.1 and Figure 9.6.

Figure 9.11 Mathematical model of human input frequency (H) and robotic decision-making ability (R).

Figure 9.A.1 Detailed overview of the status quo system configuration of automation.

Figure 9.A.2 Detailed overview of the remote control system configuration of automation.

Figure 9.A.3 Detailed overview of the tele-operation system configuration of automation.

Figure 9.A.4 Detailed overview of the driver warning system configuration of automation.

Figure 9.A.5 Detailed overview of the driver assist system configuration of automation.

Figure 9.A.6 Detailed overview of the tethered leader-follower system configuration of automation.

Figure 9.A.7 Detailed overview of the un-tethered leader-follower system configuration of automation.

Figure 9.A.8 Detailed overview of the un-tethered/unmanned/pre-driven leader-follower system configuration of automation.

Figure 9.A.9 Detailed overview of the un-tethered/unmanned/uploaded leader-follower system configuration of automation.

Figure 9.A.10 Detailed overview of the pre-recorded “breadcrumb” waypoint system configuration of automation.

Figure 9.A.11 Detailed overview of the uploaded “breadcrumb” waypoint system configuration of automation.

Figure 9.A.12 Detailed overview of the uploaded “breadcrumbs” with route suggestion full automation system configuration of automation.

Figure 9.A.13 Detailed overview of the self-determining full automation system configuration of automation.

Chapter 10

Figure 10.1 Ishikawa diagram for factor identification.

Chapter 11

Figure 11.1 DoD 5000 acquisition framework.

Figure 11.2 COSYSMO system life cycle phases.

Figure 11.3 Product breakdown structure of a typical UMAS.

Figure 11.4 Operating environment technology development timeline (2013–2030).

Figure 11.5 UMAS training objectives (2013–2030).

Figure 11.6 Squad Mission Support System (SMSS™).

Chapter 12

Figure 12.1 Simplified logistics network diagram.

Chapter 13

Figure 13.1 The basic combat network represents the simplest situation in which one side can influence another.

Figure 13.2 The simplest complete combat network represents all the ways in which Sensors, Deciders, Influencers, and Targets interact meaningfully with each other.

Figure 13.3 An adjacency matrix for the simplest complete combat network.

Figure 13.4 The two meaningfully different configurations of a 4-3-4-1 network.

Figure 13.5 An adjacency matrix for one of the 42 meaningfully different configurations of a 7-3-7-1 network.

Figure 13.6 The probability of a BLUE win for each of the 42 BLUE configurations against each of the 42 RED configurations.

Figure 13.7 The average probability of a BLUE win by λ

PFE

for 42 configurations of a 7-3-7-1 BLUE network.

Figure 13.8 The average probability of a BLUE win by λ

PFE

for 78 configurations of an 8-3-8-1 BLUE network.

Figure 13.9 The average probability of a BLUE win by λ

PFE

for 95 configurations of a 9-5-9-1 BLUE network.

Figure 13.10 The robustness values of the 95 configurations of a 9-5-9-1 BLUE network.

Chapter 14

Figure 14.1 Percentage of batters hitting percentage of triples.

Figure 14.2 Percentage of runners stealing percentage of bases.

Figure 14.3 Comparison of the double cumulative distribution of various statistics. X-axis portrays percentage of cumulative success and the Y-axis is percentage of cumulative players.

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OPERATIONS RESEARCH FOR UNMANNED SYSTEMS

Edited by

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Library of Congress Cataloging-in-Publication Data

Names: Cares, Jeffrey R., editor. | Dickmann, Jr., John Q., editor.Title: Operations research for unmanned systems / edited by Jeffrey R. Cares and John Q. Dickmann, Jr.Description: Chichester, UK ; Hoboken, NJ : John Wiley & Sons, 2016. | Includes bibliographical references and index. | Description based on print version record and CIP data provided by publisher; resource not viewed.Identifiers: LCCN 2015036918 (print) | LCCN 2015033015 (ebook) | ISBN 9781118918913 (Adobe PDF) | ISBN 9781118918920 (ePub) | ISBN 9781118918944 (cloth)Subjects: LCSH: Autonomous vehicles–Industrial applications. | Drone aircraft–Industrial applications. | Vehicles, Remotely piloted–Industrial applications.Classification: LCC TL152.8 (print) | LCC TL152.8 .O64 2016 (ebook) | DDC 629.04/6–dc23LC record available at http://lccn.loc.gov/2015036918

A catalogue record for this book is available from the British Library.

About the Contributors

Rajan BattaAssociate Dean for Research and Graduate Education, School of Engineering and Applied SciencesSUNY Distinguished Professor, Department of Industrial & Systems EngineeringUniversity at Buffalo (State University of New York), NYUSA

Fred D. J. BowdenLand Capability Analysis BranchJoint and Operations Analysis DivisionDSTO-E, Edinburgh, SAAustralia

Shannon R. BowlingDean of the college of Engineering Technology and Computer ScienceBluefield State CollegeBluefield, WVUSA

Juan José Vales Bravo,Environmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, SevilleSpain

Elena María Méndez CaballeroEnvironmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, SevilleSpain

Jeffrey R. CaresCaptain, US Navy (Ret.)Alidade Inc.USA

Francisco Cáceres ClaveroRegional Ministry of Environment and Spatial PlanningAvenida Manuel Siurot, SevilleSpain

Kelly CohenDepartment of Aerospace Engineering and Engineering MechanicsCollege of EngineeringUniversity of CincinnatiCincinnati, OHUSA

Andrew W. CouttsLand Capability Analysis BranchJoint and Operations Analysis DivisionDSTO-E, Edinburgh, SAAustralia

Agamemnon CrassidisThe Kate Gleason College of EngineeringRochester Institute of TechnologyRochester, NYUSA

Sean DellerJoint and Coalition Warfighting (J7)Washington, DCUSA

Richard M. DexterLand Capability Analysis BranchJoint and Operations Analysis DivisionDSTO-E, Edinburgh, SAAustralia

John Q. Dickmann, Jr.Sonalysts Inc.USA

Fernando Giménez de AzcárateRegional Ministry of Environment and Spatial PlanningAvenida Manuel Siurot, SevilleSpain

Luke FinlayLand Capability Analysis BranchJoint and Operations Analysis DivisionDSTO-E, Edinburgh, SAAustralia

Matthew J. HencheyHerren AssociatesWashington, DCUSA

Patrick Chisan HewDefence Science and Technology OrganisationHMAS StirlingGarden Island, WAAustralia

Raymond R. HillDepartment of Operational SciencesUS Air Force Institute of Technology/ENSWright-Patterson AFBDayton, OHUSA

David HopkinSection HeadMaritime Asset ProtectionDefence Research and Development Canada – AtlanticDartmouth, NSCanada

Keirin JoyceUniversity of New South Wales at the Australian Defence Force Academy (UNSW Canberra)Australia

Mark KarwanDepartment of Industrial and Systems EngineeringUniversity at Buffalo (State University of New York), NYUSA

Elad KivelevitchDepartment of Aerospace Engineering and Engineering MechanicsCollege of EngineeringUniversity of CincinnatiCincinnati, OHUSA

Manish KumarDepartment of Mechanical, Industrial & Manufacturing EngineeringUniversity of ToledoToledo, OHUSA

José Manuel Moreira MadueñoRegional Ministry of Environment and Spatial PlanningAvenida Manuel Siurot, SevilleSpain

David M. MahalakApplied Logistics Integration ConsultingDallas, PAUSA

Gregoria Montoya ManzanoEnvironmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, Seville Spain

Raquel Prieto MolinaEnvironmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, SevilleSpain

Bao NguyenSenior ScientistDefence Research and Development CanadaCentre for Operational Research and AnalysisOttawa, ONCanada

Ben PietschLand Capability Analysis BranchJoint and Operations Analysis DivisionDSTO-E, Edinburgh, SAAustralia

Ghaith RabadiDepartment of Engineering Management & Systems Engineering (EMSE)Old Dominion UniversityNorfolk, VAUSA

Laura Granado RuizEnvironmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, SevilleSpain

Thomas R. Ryan, Jr. (Tommy Ryan)Department of Systems EngineeringUnited States Military AcademyWest Point, NYUSA

Irene Rosa Carpintero SalvoEnvironmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, Seville Spain

Isabel Pino SerratoEnvironmental Information Network of AndalusiaEnvironment and Water AgencyJohan Gutenberg, Seville Spain

Denis R. ShineLand Capability Analysis BranchJoint and Operations Analysis DivisionDSTO-E, Edinburgh, SAAustralia

Brian B. StoneDepartment of Operational Sciences, US Air Force Institute of Technology/ENSWright-Patterson AFB, OHUSA

Huang Teng TanDepartment of Operational SciencesUS Air Force Institute of Technology/ENSWright-Patterson AFBDayton, OHUSA

Andreas TolkThe MITRE CorporationUSA

Ricardo ValerdiDepartment of Systems and Industrial EngineeringUniversity of ArizonaTucson, AZUSA

Handson YipSupreme Allied Command TransformationStaff Element EuropeSHAPE, MonsBelgium

Acknowledgements

At the end of any major literary undertaking such as writing this book, authors take time to thank the many people who support their work. In this case, however, the editors are supporting, and the main effort, the real work that went into this book, was accomplished by the contributing authors. We justifiably thank them for the time and effort spent to create each chapter, and the patience they displayed with a lengthy submission and editing process. There is no doubt, moreover, that each of them has their own thanks to convey, but it would have made the process much longer and more complicated to include their own thanks here. To those who also supported the various authors in their independent efforts, we are likewise truly grateful.

We are also grateful for the support provided by the Military Operations Research Society (MORS). Four of the chapters, those by Han and Hill; Nguyen, Hopkin and Yip; Henchey, Batta, Karwan and Crassidis; and Deller, Rabadi, Tolk and Bowling first appeared in their research publication, MORS Journal. It is with their kind permission that they are reprinted here. It speaks loudly for the MORS Journal that some of the cutting edge research on this important topic was first presented in their pages.

1Introduction

Jeffrey R. Cares1 and John Q. Dickmann, Jr.2

1Captain, US Navy (Ret.), Alidade Inc., USA

2Sonalysts Inc., USA

1.1 Introduction

Given all the attention and investment recently bestowed on unmanned systems, it might seem surprising that this book does not already exist. Even the most cursory internet search on this topic will show professional journal articles, industry symposia proceedings, and technical engineering texts conveying broad interest, substantial investment, and aggressive development in unmanned systems. Yet an internet bookstore or library search for “operations research” combined with “unmanned systems” will come up blank. This book will indeed be the first of its kind.

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!